Computing phonological generalization over real speech exemplars

Though it has attracted growing attention from phonologists and phoneticians, Exemplar Theory (e.g. Bybee, 2001) has hitherto lacked an explicit production model that can apply to speech signals. An adequate model must be able to generalize; but this presents the problem of how to generate an output that generalizes over a collection of unique, variable-length signals. Rather than resorting to a priori phonological units such as phones, we adopt a dynamic programming approach, using an optimization criterion that is sensitive to the frequency of similar subsequences within other exemplars: the Phonological Exemplar-Based Learning System. We show that PEBLS displays pattern-entrenchment behaviour, central to Exemplar Theory's account of phonologization.

Research Highlights
► Phonological Exemplar-Based Learning System (PEBLS) is the first explicit exemplar model that can handle variable-length signals of real speech.
► PEBLS outputs reasonably natural-sounding synthetic speech.
► PEBLS’ confidence-sensitive dynamic programming approach allows exemplar-based generalization of phonological patterns without recourse to a priori phonological units.